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About the Project An exciting PhD project on the effects of heat transfer of transitional compressible boundary layers will be carried out under the UK Hypersonics Doctoral Network, which has been
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(e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects of machine learning such as
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Rolls-Royce UltraFan has completely changed the architecture of the compression system. This has opened the design space and means that new technologies that can improve performance and reduce fuel burn
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and interpretability, analogous to RAG (Retrieval-Augmented Generation) in LLMs Investigating methods for improving AI model sustainability, e.g. model compression techniques (such as quantization and
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and servers such as gradient compression, asynchronous training, reduced synchronization frequency, semantic communication, and design of new application and transport layer protocols. Data management
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statistics This PhD project falls under the collaboration between Research Thrust RT2 Physics-based models, and Research Thrust RT3 on representation, compression, learning, and inference. For long-distance
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on constrained platforms using techniques such as model compression, quantization, and hardware-aware neural network design. Investigating mechanisms that protect the integrity and reliability of deployed AI
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model pre-training and multimodal adaptation to architectures and compression for edge deployment while targeting real-world validation in domains like HealthTech, smart industry, and autonomous mobility
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of a delamination can seriously reduce the strength and stiffness of a laminate especially under compressive buckling loads, potentially leading to catastrophic failure. We have developed new generation
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lightweight AI models suitable for real-time execution on constrained platforms using techniques such as model compression, quantization, and hardware-aware neural network design. Investigating mechanisms